Let’s define some vectors which can be used for demonstrations:
manyNumbers <- sample( 1:1000, 20 )
manyNumbers
[1] 808 750 737 799 668 383 883 652 94 482 344 543 391 283 152 20 582 910 493 474
manyNumbersWithNA <- sample( c( NA, NA, NA, manyNumbers ) )
manyNumbersWithNA
[1] 20 737 750 383 482 652 582 94 344 NA 543 474 493 152 799 283 910 668 808 NA 391 NA 883
duplicatedNumbers <- sample( 1:5, 10, replace = TRUE )
duplicatedNumbers
[1] 3 4 5 5 4 1 5 3 4 4
letters
[1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v" "w" "x" "y" "z"
LETTERS
[1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S" "T" "U" "V" "W" "X" "Y" "Z"
mixedLetters <- c( sample( letters, 5 ), sample( LETTERS, 5 ) )
mixedLetters
[1] "d" "l" "j" "n" "v" "J" "Z" "E" "H" "W"
manyNumbersWithNA instead of manyNumbers.all( manyNumbers <= 1000 )
[1] TRUE
all( manyNumbers <= 500 )
[1] FALSE
any( manyNumbers > 1000 )
[1] FALSE
any( manyNumbers > 500 )
[1] TRUE
all( !is.na( manyNumbers ) )
[1] TRUE
any( is.na( manyNumbers ) )
[1] FALSE
Input: logical vector Output: vector of numbers (positions)
which( manyNumbers > 900 )
[1] 18
which( manyNumbersWithNA > 900 )
[1] 17
which( is.na( manyNumbersWithNA ) )
[1] 10 20 22
manyNumbers[ manyNumbers > 900 ] # indexing by logical vector
[1] 910
manyNumbers[ which( manyNumbers > 900 ) ] # indexing by positions
[1] 910
somePositions <- which( manyNumbers > 900 )
manyNumbers[ somePositions ]
[1] 910
"A" %in% LETTERS
[1] TRUE
c( "X", "Y", "Z" ) %in% LETTERS
[1] TRUE TRUE TRUE
all( c( "X", "Y", "Z" ) %in% LETTERS )
[1] TRUE
all( mixedLetters %in% LETTERS )
[1] FALSE
any( mixedLetters %in% LETTERS )
[1] TRUE
mixedLetters[ mixedLetters %in% LETTERS ]
[1] "J" "Z" "E" "H" "W"
mixedLetters[ !( mixedLetters %in% LETTERS ) ]
[1] "d" "l" "j" "n" "v"
manyNumbers %in% 300:600
[1] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE
[19] TRUE TRUE
which( manyNumbers %in% 300:600 )
[1] 6 10 11 12 13 17 19 20
sum( manyNumbers %in% 300:600 )
[1] 8
NAsif_else( manyNumbersWithNA >= 500, "large", "small" )
[1] "small" "large" "large" "small" "small" "large" "large" "small" "small" NA "large" "small" "small" "small"
[15] "large" "small" "large" "large" "large" NA "small" NA "large"
if_else( manyNumbersWithNA >= 500, "large", "small", "UNKNOWN" )
[1] "small" "large" "large" "small" "small" "large" "large" "small" "small" "UNKNOWN" "large"
[12] "small" "small" "small" "large" "small" "large" "large" "large" "UNKNOWN" "small" "UNKNOWN"
[23] "large"
# here integer 0L is needed instead of real 0.0
# manyNumbersWithNA contains integer numbers and the method complains
if_else( manyNumbersWithNA >= 500, manyNumbersWithNA, 0L )
[1] 0 737 750 0 0 652 582 0 0 NA 543 0 0 0 799 0 910 668 808 NA 0 NA 883
unique( duplicatedNumbers )
[1] 3 4 5 1
unique( c( NA, duplicatedNumbers, NA ) )
[1] NA 3 4 5 1
duplicated( duplicatedNumbers )
[1] FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
which.max( manyNumbersWithNA )
[1] 17
manyNumbersWithNA[ which.max( manyNumbersWithNA ) ]
[1] 910
which.min( manyNumbersWithNA )
[1] 1
manyNumbersWithNA[ which.min( manyNumbersWithNA ) ]
[1] 20
range( manyNumbersWithNA, na.rm = TRUE )
[1] 20 910
manyNumbersWithNA
[1] 20 737 750 383 482 652 582 94 344 NA 543 474 493 152 799 283 910 668 808 NA 391 NA 883
sort( manyNumbersWithNA )
[1] 20 94 152 283 344 383 391 474 482 493 543 582 652 668 737 750 799 808 883 910
sort( manyNumbersWithNA, na.last = TRUE )
[1] 20 94 152 283 344 383 391 474 482 493 543 582 652 668 737 750 799 808 883 910 NA NA NA
sort( manyNumbersWithNA, na.last = TRUE, decreasing = TRUE )
[1] 910 883 808 799 750 737 668 652 582 543 493 482 474 391 383 344 283 152 94 20 NA NA NA
manyNumbersWithNA[1:5]
[1] 20 737 750 383 482
order( manyNumbersWithNA[1:5] )
[1] 1 4 5 2 3
rank( manyNumbersWithNA[1:5] )
[1] 1 4 5 2 3
sort( mixedLetters )
[1] "d" "E" "H" "j" "J" "l" "n" "v" "W" "Z"
manyDuplicates <- sample( 10:15, 10, replace = TRUE )
rank( manyDuplicates )
[1] 4.5 8.0 6.5 1.5 1.5 3.0 9.5 4.5 6.5 9.5
rank( manyDuplicates, ties.method = "min" )
[1] 4 8 6 1 1 3 9 4 6 9
rank( manyDuplicates, ties.method = "random" )
[1] 5 8 7 1 2 3 9 4 6 10
v <- c( -1, -0.5, 0, 0.5, 1, rnorm( 10 ) )
v
[1] -1.0000000 -0.5000000 0.0000000 0.5000000 1.0000000 0.8974966 -1.0058081 -2.0252153 0.1619413 -0.8163879
[11] -0.2233431 0.2304619 0.7885859 -1.1538665 1.0865769
round( v, 0 )
[1] -1 0 0 0 1 1 -1 -2 0 -1 0 0 1 -1 1
round( v, 1 )
[1] -1.0 -0.5 0.0 0.5 1.0 0.9 -1.0 -2.0 0.2 -0.8 -0.2 0.2 0.8 -1.2 1.1
round( v, 2 )
[1] -1.00 -0.50 0.00 0.50 1.00 0.90 -1.01 -2.03 0.16 -0.82 -0.22 0.23 0.79 -1.15 1.09
floor( v )
[1] -1 -1 0 0 1 0 -2 -3 0 -1 -1 0 0 -2 1
ceiling( v )
[1] -1 0 0 1 1 1 -1 -2 1 0 0 1 1 -1 2
heights <- c( Amy = 166, Eve = 170, Bob = 177 )
heights
Amy Eve Bob
166 170 177
names( heights )
[1] "Amy" "Eve" "Bob"
names( heights ) <- c( "AMY", "EVE", "BOB" )
heights
AMY EVE BOB
166 170 177
heights[[ "EVE" ]]
[1] 170
expand_grid( x = c( 1:3, NA ), y = c( "a", "b" ) )
# A tibble: 8 × 2
x y
<int> <chr>
1 1 a
2 1 b
3 2 a
4 2 b
5 3 a
6 3 b
7 NA a
8 NA b
combn( c( "a", "b", "c", "d", "e" ), m = 2, simplify = TRUE )
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a" "a" "a" "a" "b" "b" "b" "c" "c" "d"
[2,] "b" "c" "d" "e" "c" "d" "e" "d" "e" "e"
combn( c( "a", "b", "c", "d", "e" ), m = 3, simplify = TRUE )
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a" "a" "a" "a" "a" "a" "b" "b" "b" "c"
[2,] "b" "b" "b" "c" "c" "d" "c" "c" "d" "d"
[3,] "c" "d" "e" "d" "e" "e" "d" "e" "e" "e"
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